What are the most common reasoning frameworks for data science?

As quoted by Sir Arthur Conan Doyle, "When you have eliminated the impossible, whatever remains, however improbable, must be the truth". Deductive reasoning plays a crucial role in data science by helping analysts and data scientists draw logical conclusions from available information. Deductive reasoning involves starting with general premises or principles and then deriving specific conclusions based on them. In the context of data science, deductive reasoning can be beneficial in several ways and techniques including Hypothesis testing, Logical frameworks, FTA, Root cause analysis etc. In my experience, I have used deductive reasoning in solving some of the most complex cases of root cause analysis and arrived at the correct conclusion

Abductive reasoning also refers to the process you employ to explain why the observations are not conforming to an existing theory or set of assumptions behind the theory. You are able to better explain the observations, if an additional assumption is added to the assumption set and build a new theory. It is the data, that is forcing you to think of a new theory or explanation. You are doing the work of a data scientist to generalize the pattern found in the data. But you are using existing theory also to make your explanation more sound and rational.

By drawing parallels between known domains and new challenges, it allows for the transfer of insights and methodologies, thus facilitating learning and problem-solving. For instance, in understanding complex systems like neural networks, analogies with the human brain provide an intuitive framework, simplifying concepts like neurons and synaptic connections. This reasoning style is instrumental in data science for adapting existing models to novel situations, enabling data scientists to apply proven techniques creatively and efficiently to different scenarios. Analogical reasoning, thus, acts as a bridge between the familiar and the unknown, enhancing the cognitive toolkit of a data scientist.

Establecer relaciones causa efecto de manera cuantitativa es una aportación muy útil a la hora de tomar decisiones ya que deja de lado el sesgo que cualquier persona pueda tener. Cuando se dispone de una colección de datos prolongada en el tiempo es fundamental establecer esta relación y el impacto entre valores. Además, si los situamos en el tiempo permite establecer los factores externos que han podido causar impacto en la tendencia. Por tanto. Un buen análisis de correlación acusa efecto cuantitativo puede contribuir de manera objetiva a predecir posibles comportamientos a futuro. La ciencia de datos, dota de objetividad las decisiones

Consider the following aspects to deepen your understanding and application: - Statistical Reasoning: Understanding and applying statistical methods and principles to interpret data and make inferences. - Predictive Reasoning: Using historical data to make predictions about future events or trends, often employed in machine learning models. - Bayesian Reasoning: Applying Bayesian methods to update the probability of a hypothesis as more evidence or information becomes available.

One of the most striking aspects of this theme is the scarcity of educational institutions that provide an intentional and well-structured focus on developing reasoning skills. Many institutions assume that these skills will naturally evolve as a byproduct of other learning experiences, but this assumption often proves inaccurate in real-world scenarios. Consequently, we encounter graduate students whose reasoning abilities are not fully honed, leading them to struggle with a profound understanding of data science or to reach conclusions that initially appear valid but lack the necessary coherent arguments to support them effectively. Students would benefit from a more explicit approach aimed at cultivating these specific skills.

In my opinion, considering the most suitable option depends on the industry where the leader involved in. Understanding the market and the need at certain point of time while maintaining the futuristic goal and direction in mind keeps the leader on track. It said * wards off evil takes precedence over bringing benefits *, well, I don’t agree totally because sometimes staying in the safe zone will result unpleasant consequences sooner or later. Adventures are joyful yet must be studied brilliantly.

In my opinion, considering the most suitable option depends on the industry where the leader involved in. Understanding the market and the need at certain point of time while maintaining the futuristic goal and direction in mind keeps the leader on track. It said * wards off evil takes precedence over bringing benefits *, well, I don’t agree totally because sometimes staying in the safe zone will result unpleasant consequences sooner or later. Adventures are joyful yet must be studied brilliantly.